On-chain reward an agent can EARN: every $BOBAI buy >= $100 (single swap) auto-mints a capped-supply collectible NFT to the buyer wallet — no claim, no signup. Live remaining supply per buy-tier and rarity, plus the exact steps to earn one.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safe read-only nature is established. The description adds meaningful behavioral context beyond annotations: the NFT is auto-minted with no claim/signup, the supply is capped, and the tool reports live remaining supply by tier and rarity. This gives the agent a clear picture of the operation's semantics without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff. The first sentence establishes what the tool is about and the key earning condition; the second sentence states the concrete information provided. All details—threshold, auto-mint, no claim, capped supply, live data, steps—are packed efficiently and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only informational tool, the description covers the essentials: what the reward is, the qualifying condition, and what the tool returns (live remaining supply per tier/rarity and exact earning steps). It does not enumerate all tiers or rarity names, but that level of detail is not necessary for invocation. The absence of an output schema makes the return summary in the description particularly valuable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters and the schema has no properties, so there are no parameter semantics to document. The description's mention of buy-tier and rarity relates to the output content, not input parameters, which is appropriate here. Based on the 0-parameter baseline, the description adequately handles this dimension.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's subject: an on-chain NFT reward tied to $BOBAI purchases, with a specific threshold ($100 single swap), auto-minting behavior, and the information it provides (remaining supply per tier/rarity and earning steps). Although there is no explicit verb like 'get', the description is specific enough to distinguish it from all sibling tools, including bobai_purchase_guide and bobai_activity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied: an agent interested in the NFT drop, its remaining supply, or the steps to earn one would use this tool. However, there is no explicit when-to-use or when-not-to-use guidance, and no sibling tools are named as alternatives. The description does not help disambiguate against similar bobai-related informational tools beyond the NFT-specific subject matter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools operate on overlapping data: bobai_burned, bobai_activity, and bobai_smart_money all report burn-related metrics, while bobai_token_info, bobai_circulating_supply, and bobai_burned overlap on supply numbers. The long descriptions mitigate this, but the purpose boundaries are less crisp than ideal. The BSC pool tools are more easily told apart.
Most token tools use a consistent bobai_* snake_case prefix, but the set mixes conventions: bnb_agent_*, bsc_pool_scan, find_agents_on_bnb_chain, and pancakeswap_fee_tiers break the pattern. The names are still readable and mostly noun-oriented. There is no chaotic mixing of camelCase or vague verbs, but the pattern is not uniform.
19 tools sits in the borderline heavy range for a token research server. Many are focused data views, but some could be consolidated, such as bobai_token_info with circulating supply/burned, or the informational guide/links/tokenomics cluster. Each tool has a defensible role, but the overall surface feels padded.
Core workflows are well covered: price, liquidity, DEX parameters, purchase guidance, burns, wallet balances, and BNB agent discovery. Missing historical price/volume, holder distributions, or detailed per-agent profiles, but these are minor gaps rather than dead ends. The server gives an agent what it needs to understand and interact with $BOBAI.